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Ideal for comprehensive analysis requiring large context windows. Uses Gemini 3 Pro by default for state-of-the-art reasoning and coding.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fgemini\u002FSKILL.md",[234],{"categoryNo":235,"categoryName":236,"taxonomyType":153,"taxonomyTypeDesc":154},"01JBSEEDCAT000000000000007","代码审查",[230,238,215,239,240,241,242,243,244,245,218,246,247,248,249,219,250,251,252,253,226],"code review","cli","gemini cli","windows","run","code","reasoning","state","plan","coding","plan review","context","Art","default","processing","for",{"skillNo":255,"name":256,"nameZh":18,"description":257,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":258,"categories":259,"keywords":267,"publishedAt":227},"01M6ADQ8JPX5M07N5VVJ5SWKTN","gemini-api-agent-platform","Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK for enterprise AI applications. 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NVIDIA's enterprise-grade platform with container-first architecture for reproducible benchmarking.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fevaluation-nemo-evaluator\u002FSKILL.md",[382,383],{"categoryNo":261,"categoryName":262,"taxonomyType":153,"taxonomyTypeDesc":154},{"categoryNo":384,"categoryName":385,"taxonomyType":153,"taxonomyTypeDesc":154},"01JBSEEDCAT000000000000011","运维与部署",[387,388,389,390,330,391,216,392,393,280,394,395,396,397,398,399,400,282,401,402,403,404,271,405,406,407,226],"Evaluation","evaluation nemo evaluator","evaluator","nemo evaluator","docker","architecture","benchmarking","HumanEval","NeMo","NVIDIA","MMLU","Multi-Backend","Slurm","Reproducible","Safety","launcher","across","local","Scalable","harnesses","hpc",{"skillNo":409,"name":410,"nameZh":18,"description":411,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":412,"categories":413,"keywords":414,"publishedAt":227},"01M6ADPKWXEGED5MVZCDPNC2A7","evaluation-lm-evaluation-harness","Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). 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Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fevaluation-bigcode-evaluation-harness\u002FSKILL.md",[430],{"categoryNo":10,"categoryName":13,"taxonomyType":11,"taxonomyTypeDesc":12},[415,432,433,434,216,353,393,243,435,387,436,394,437,438,439,440,441,314,403,442,358,443,226],"evaluation bigcode evaluation harness","bigcode evaluation harness","testing","metrics","Code Generation","MBPP","MultiPL-E","Pass@k","BigCode","Code Models","project","multipl",{"skillNo":445,"name":446,"nameZh":18,"description":447,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":448,"categories":449,"keywords":450,"publishedAt":227},"01M6ADPD8KEZFCFW15EK48WPEF","emerging-techniques-speculative-decoding","Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. 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Use when training large-scale models with limited compute (5× cost reduction vs dense models), implementing sparse architectures like Mixtral 8x7B or DeepSeek-V3, or scaling model capacity without proportional compute increase. Covers MoE architectures, routing mechanisms, load balancing, expert parallelism, and inference optimization.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Femerging-techniques-moe-training\u002FSKILL.md",[],[301,477,478,453,479,454,455,216,480,481,370,303,314,420,317,482,483,358,484,485,486,487,488,489,490,226],"emerging techniques moe training","moe","moe training","capacity","routing","scaling","architectures","Mixture Of Experts","Sparse Models","Expert Parallelism","Mixtral","Efficient Training","experts","implementing",{"skillNo":492,"name":493,"nameZh":18,"description":494,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":495,"categories":496,"keywords":497,"publishedAt":227},"01M6ADP7XGBJN0EFQ2TYXB19C2","emerging-techniques-model-pruning","Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsity with minimal accuracy loss, or enabling faster inference on hardware accelerators. Covers unstructured pruning, structured pruning, N:M sparsity, magnitude pruning, and one-shot methods.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Femerging-techniques-model-pruning\u002FSKILL.md",[],[498,420,453,499,454,455,216,500,302,457,314,358,501,502,503,504,505,506,507,508,509,321,510,226],"emerging techniques model pruning","Model Pruning","compression","Model Compression","Wanda","SparseGPT","Sparsity","N:M Sparsity","One-Shot Pruning","Structured Pruning","Unstructured Pruning","unstructured","reduce",{"skillNo":512,"name":513,"nameZh":18,"description":514,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":515,"categories":516,"keywords":517,"publishedAt":227},"01M6ADP534Y95ZS87V4WRJG7BA","emerging-techniques-model-merging","Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific expertise (math + coding + chat), improving performance beyond single models, or experimenting rapidly with model variants. Covers SLERP, TIES-Merging, DARE, Task Arithmetic, linear merging, and production deployment strategies.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Femerging-techniques-model-merging\u002FSKILL.md",[],[518,420,453,519,454,520,216,521,456,277,522,314,523,524,460,525,526,527,528,529,530,531,226],"emerging techniques model merging","Model Merging","performance","merge","linear","math","specialized","fusion","Mergekit","SLERP","TIES","DARE","expertise","variants",{"skillNo":533,"name":534,"nameZh":18,"description":535,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":536,"categories":537,"keywords":538,"publishedAt":227},"01M6ADP25B13XWMHB68FZKKVZ2","emerging-techniques-long-context","Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Femerging-techniques-long-context\u002FSKILL.md",[],[539,249,540,541,453,454,330,216,241,542,543,544,545,314,546,460,252,490,547,548,226,549],"emerging techniques long context","long context","long","documents","yarn","embeddings","encoding","flash","interpolation","limits","extending",{"skillNo":551,"name":552,"nameZh":18,"description":553,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":554,"categories":555,"keywords":557,"publishedAt":227},"01M6ADNZA7GV6GN2HQGXTHHMA9","emerging-techniques-knowledge-distillation","Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Femerging-techniques-knowledge-distillation\u002FSKILL.md",[556],{"categoryNo":212,"categoryName":213,"taxonomyType":153,"taxonomyTypeDesc":154},[558,559,560,453,561,454,562,455,520,216,301,277,563,500,564,565,420,482,566,567,568,569,570,571,572,573,226],"emerging techniques knowledge distillation","knowledge","distillation","Knowledge Distillation","large language models","gpt","transfer","open","Teacher-Student","MiniLLM","Reverse KLD","Soft Targets","Temperature Scaling","Logit Distillation","Model Transfer","student",{"skillNo":575,"name":576,"nameZh":18,"description":577,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":578,"categories":579,"keywords":580,"publishedAt":227},"01M6ADNWW9BENR4P4HJMPFBPTT","distributed-training-ray-train","Distributed training orchestration across clusters. Scales PyTorch\u002FTensorFlow\u002FHuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-ray-train\u002FSKILL.md",[],[581,301,582,583,584,585,586,587,216,588,589,314,590,298,591,403,482,592,593,226,594,595],"distributed","Distributed Training","distributed training ray train","Ray","train","Ray Train","tensorflow","orchestration","pytorch","elastic","node","fault tolerance","tolerance","running","Clusters",{"skillNo":597,"name":598,"nameZh":18,"description":599,"descriptionZh":18,"repositoryFullName":201,"ownerLogin":202,"ownerAvatarUrl":203,"ownerAccountNo":204,"ownerDisplayName":205,"repositoryNo":206,"starsCount":207,"licenseSpdxId":208,"licenseFileUrl":18,"sourceHtmlUrl":600,"categories":601,"keywords":603,"publishedAt":227},"01M6ADNSS6V7CJ2AJXG6YDPRKJ","distributed-training-pytorch-lightning","High-level PyTorch framework with Trainer class, automatic distributed training (DDP\u002FFSDP\u002FDeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-pytorch-lightning\u002FSKILL.md",[602],{"categoryNo":10,"categoryName":13,"taxonomyType":11,"taxonomyTypeDesc":12},[581,589,301,604,582,605,606,216,243,607,608,370,314,270,609,610,611,612,613,614,405,615,340,226],"lightning","distributed training pytorch lightning","pytorch lightning","best practices","framework","clean","FSDP","DDP","Training Framework","High-Level API","Callbacks","loops",190,18]